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Visual Recommendations for Network Navigation
Author(s) -
Crnovrsanin Tarik,
Liao Isaac,
Wu Yingcai,
Ma KwanLiu
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.01957.x
Subject(s) - computer science , visibility , collaborative filtering , graph , process (computing) , node (physics) , visualization , clutter , visibility graph , graph drawing , data mining , human–computer interaction , recommender system , artificial intelligence , machine learning , theoretical computer science , telecommunications , radar , physics , geometry , mathematics , structural engineering , regular polygon , optics , engineering , operating system
Understanding large, complex networks is important for many critical tasks, including decision making, process optimization, and threat detection. Existing network analysis tools often lack intuitive interfaces to support the exploration of large scale data. We present a visual recommendation system to help guide users during navigation of network data. Collaborative filtering, similarity metrics, and relative importance are used to generate recommendations of potentially significant nodes for users to explore. In addition, graph layout and node visibility are adjusted in real‐time to accommodate recommendation display and to reduce visual clutter. Case studies are presented to show how our design can improve network exploration.